The World Health Organization has identified cardiovascular disease as the leading cause of non-accidental deaths in the world. A heart is identified as diseased when it is not operating efficiently. Early diagnosis of the disease can impact treatment and improve a patient's outcome. An early sign of a diseased heart is a reduction of the heart's pumping ability, which can be measured by doing functional evaluations. These are normally focused on the ability of the ventricles to pump blood to the lungs (right ventricle) or to the entire body (left ventricle).
Non-invasive imaging modalities such as cardiac magnetic resonance (CMR) have allowed the use of quantitative methods for ventricular functional evaluation. The evaluation still requires the tracing of the ventricles, such as at end-diastole and end-systole, in four chamber (4CH) and short axis (SAX) CMR images as shown in FIG. 1.
Even though manual tracing is still considered the gold standard, it is prone to intra- and inter-observer variability and is time-consuming. Therefore, substantial research work has been focused on the development of semi- and fully automated ventricle segmentation algorithms.
In 2009 the Medical Image Computing and Computer-Assisted Intervention (MICCAI) Conference issued a challenge for short axis left ventricle segmentation. A semi-automated technique using polar dynamic programming generated results that were within human variability. This is because a simple path in a polar coordinate system yields a round object in the Cartesian grid and the left ventricle can be approximated as a round object. In 2012 there was an MICCAI right ventricle segmentation challenge, but no polar dynamic programming algorithms were used. This is because polar dynamic programming is best for segmenting round shapes and the right ventricle has a complex shape.